




'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a       :						Optional[Any]								=		logging.get_logger(__name__)

a       :						List[str]								=		{}



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			"""llama"""
			__SCREAMING_SNAKE_CASE    =			["""past_key_values"""]





			def __init__(      self						:    Optional[int]      ,		a_						:    str=32_000      ,		a_						:    Any=4_096      ,		a_						:    List[Any]=11_008      ,		a_						:    List[str]=32      ,		a_						:    Any=32      ,		a_						:    str=None      ,		a_						:    str="silu"      ,		a_						:    Optional[int]=2_048      ,		a_						:    Optional[Any]=0.02      ,		a_						:    int=1e-6      ,		a_						:    Any=True      ,		a_						:    List[Any]=0      ,		a_						:    int=1      ,		a_						:    List[Any]=2      ,		a_						:    List[Any]=1      ,		a_						:    Union[str, Any]=False      ,		a_						:    Optional[int]=None      ,		**a_						:    Optional[int]      ,		):



						"""simple docstring"""




						__snake_case			=		vocab_size
						__snake_case			=		max_position_embeddings
						__snake_case			=		hidden_size
						__snake_case			=		intermediate_size
						__snake_case			=		num_hidden_layers
						__snake_case			=		num_attention_heads

						# for backward compatibility
						if num_key_value_heads is None:
									__snake_case			=		num_attention_heads

						__snake_case			=		num_key_value_heads
						__snake_case			=		hidden_act
						__snake_case			=		initializer_range
						__snake_case			=		rms_norm_eps
						__snake_case			=		pretraining_tp
						__snake_case			=		use_cache
						__snake_case			=		rope_scaling
						self._rope_scaling_validation()

						super().__init__(
						    pad_token_id=a_      ,		bos_token_id=a_      ,		eos_token_id=a_      ,		tie_word_embeddings=a_      ,		**a_      ,		)






			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						if self.rope_scaling is None:
									return

						if not isinstance(self.rope_scaling      ,		a_							) or len(self.rope_scaling							) != 2:
									raise ValueError(
									    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
									    f'''got {self.rope_scaling}'''							)
						__snake_case			=		self.rope_scaling.get("type"      ,		a_							)
						__snake_case			=		self.rope_scaling.get("factor"      ,		a_							)
						if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
									raise ValueError(
									    f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''							)
						if rope_scaling_factor is None or not isinstance(a_      ,		a_							) or rope_scaling_factor <= 1.0:
									raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''							)













"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)